WO2021080295A1 - Procédé et dispositif de conception de composé - Google Patents

Procédé et dispositif de conception de composé Download PDF

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Publication number
WO2021080295A1
WO2021080295A1 PCT/KR2020/014354 KR2020014354W WO2021080295A1 WO 2021080295 A1 WO2021080295 A1 WO 2021080295A1 KR 2020014354 W KR2020014354 W KR 2020014354W WO 2021080295 A1 WO2021080295 A1 WO 2021080295A1
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WIPO (PCT)
Prior art keywords
information
partial
partial structure
structures
mapping value
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PCT/KR2020/014354
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English (en)
Korean (ko)
Inventor
유지호
하민규
채종환
손치원
진상형
신재홍
김한조
김시우
송상옥
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주식회사 스탠다임
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Application filed by 주식회사 스탠다임 filed Critical 주식회사 스탠다임
Priority to EP20878286.2A priority Critical patent/EP4050612A4/fr
Priority to US17/770,555 priority patent/US20220383993A1/en
Publication of WO2021080295A1 publication Critical patent/WO2021080295A1/fr

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    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/70Machine learning, data mining or chemometrics
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/10Analysis or design of chemical reactions, syntheses or processes
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/30Prediction of properties of chemical compounds, compositions or mixtures
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/40Searching chemical structures or physicochemical data
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/50Molecular design, e.g. of drugs
    • GPHYSICS
    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16CCOMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/60In silico combinatorial chemistry

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  • Chemical & Material Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Crystallography & Structural Chemistry (AREA)
  • Theoretical Computer Science (AREA)
  • Computing Systems (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Medicinal Chemistry (AREA)
  • Physics & Mathematics (AREA)
  • Pharmacology & Pharmacy (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Software Systems (AREA)
  • Medical Informatics (AREA)
  • Evolutionary Computation (AREA)
  • Databases & Information Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Artificial Intelligence (AREA)
  • Analytical Chemistry (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

Un procédé de génération d'informations de composé dans un dispositif de calcul selon un mode de réalisation de la présente invention comprend les étapes consistant à : obtenir un modèle d'apprentissage concernant des informations sur une structure partielle; obtenir des informations sur une molécule source à soumettre à une modification de structure partielle; obtenir des informations sur un ensemble de structures partielles comprenant une pluralité de structures partielles de la molécule source; sélectionner une structure partielle cible à modifier parmi les structures partielles de l'ensemble de structures partielles; obtenir des informations sur la structure partielle modifiée de la structure partielle cible à l'aide du modèle d'apprentissage; et délivrer en sortie des informations de résultat dans lesquelles la structure partielle modifiée a été appliquée à la structure partielle cible dans la molécule source.
PCT/KR2020/014354 2019-10-21 2020-10-20 Procédé et dispositif de conception de composé WO2021080295A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
EP20878286.2A EP4050612A4 (fr) 2019-10-21 2020-10-20 Procédé et dispositif de conception de composé
US17/770,555 US20220383993A1 (en) 2019-10-21 2020-10-20 Method and device for designing compound

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
KR20190130769 2019-10-21
KR10-2019-0130769 2019-10-21
KR20200046192 2020-04-16
KR10-2020-0046192 2020-04-16

Publications (1)

Publication Number Publication Date
WO2021080295A1 true WO2021080295A1 (fr) 2021-04-29

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ID=75619419

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/KR2020/014354 WO2021080295A1 (fr) 2019-10-21 2020-10-20 Procédé et dispositif de conception de composé

Country Status (4)

Country Link
US (1) US20220383993A1 (fr)
EP (1) EP4050612A4 (fr)
KR (2) KR102296188B1 (fr)
WO (1) WO2021080295A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113409898A (zh) * 2021-06-30 2021-09-17 北京百度网讯科技有限公司 分子结构获取方法、装置、电子设备及存储介质

Families Citing this family (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20210287137A1 (en) * 2020-03-13 2021-09-16 Korea University Research And Business Foundation System for predicting optical properties of molecules based on machine learning and method thereof
KR20230073630A (ko) * 2021-11-19 2023-05-26 주식회사 제이엘케이바이오 화합물 최적화를 위한 장치 및 방법
WO2024063584A1 (fr) * 2022-09-21 2024-03-28 (주)신테카바이오 Procédé d'analyse de structure de liaison ligand-protéine basé sur un vecteur d'atome central d'une nouvelle plateforme de médicament à intelligence artificielle
WO2024063583A1 (fr) * 2022-09-21 2024-03-28 (주)신테카바이오 Procédé de génération de dérivés à l'aide d'une structure de poche de liaison de protéine cible par l'intermédiaire d'une plateforme de découverte de médicament à intelligence artificielle (ia)
KR20240054892A (ko) 2022-10-19 2024-04-26 주식회사 엘지화학 고분자 그래프 신경망 및 그 구현 방법
WO2024085562A1 (fr) * 2022-10-19 2024-04-25 주식회사 엘지화학 Réseau neuronal graphique de polymère et son procédé de mise en œuvre
CN116895338B (zh) * 2023-08-08 2024-02-20 盐城师范学院 树形分子研究模型的改进方法及系统
CN117116384B (zh) * 2023-10-20 2024-01-09 聊城高新生物技术有限公司 一种靶向诱导的医药分子结构生成方法

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20190087898A (ko) * 2018-01-17 2019-07-25 삼성전자주식회사 뉴럴 네트워크를 이용하여 화학 구조를 생성하는 장치 및 방법

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20190087898A (ko) * 2018-01-17 2019-07-25 삼성전자주식회사 뉴럴 네트워크를 이용하여 화학 구조를 생성하는 장치 및 방법

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
BORIS SATTAROV, IGOR I. BASKIN, DRAGOS HORVATH, GILLES MARCOU, ESBEN JANNIK BJERRUM, ALEXANDRE VARNEK: "De novo molecular design by combining deep autoencoder recurrent neural networks with generative topographic mapping", JOURNAL OF CHEMICAL INFORMATION AND MODELING, vol. 59, no. 3, 20 February 2019 (2019-02-20), pages 1182 - 1196, XP055731747, ISSN: 1549-9596, DOI: 10.1021/acs.jcim.8b00751 *
JUSTIN GILMER, SAMUEL S. SCHOENHOLZ, PATRICK F. RILEY, ORIOL VINYALS, GEORGE E. DAHL: "Neural message passing for quantum chemistry", COMPUTER SCIENCE, 4 April 2017 (2017-04-04), pages 1 - 14, XP055700744 *
KAWAI KENTARO, NAGATA NAOYA, TAKAHASHI YOSHIMASA: "De novo design of drug-like molecules by a fragment-based molecular evolutionary approach", JOURNAL OF CHEMICAL INFORMATION AND MODELING, vol. 54, no. 1, 28 December 2013 (2013-12-28), pages 49 - 56, XP055805856, ISSN: 1549-9596, DOI: 10.1021/ci400418c *
STÅHL NICLAS, FALKMAN GÖRAN, KARLSSON ALEXANDER, MATHIASON GUNNAR, BOSTRÖM JONAS: "Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design", JOURNAL OF CHEMICAL INFORMATION AND MODELING, vol. 59, no. 7, 19 June 2019 (2019-06-19), pages 3166 - 3176, XP055803313, ISSN: 1549-9596, DOI: 10.1021/acs.jcim.9b00325 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113409898A (zh) * 2021-06-30 2021-09-17 北京百度网讯科技有限公司 分子结构获取方法、装置、电子设备及存储介质
CN113409898B (zh) * 2021-06-30 2022-05-27 北京百度网讯科技有限公司 分子结构获取方法、装置、电子设备及存储介质

Also Published As

Publication number Publication date
EP4050612A4 (fr) 2023-11-15
KR20210047262A (ko) 2021-04-29
US20220383993A1 (en) 2022-12-01
KR102296188B1 (ko) 2021-09-01
KR20210110539A (ko) 2021-09-08
EP4050612A1 (fr) 2022-08-31

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